Subject Overview

An integrated introduction to how organizations use data, statistical reasoning, analytical models and digital technologies to improve decisions and create business value. The subject balances conceptual foundations, practical tools, managerial applications, AI, governance and contemporary industry practice.

Learning Goal

Students will be able to frame business problems analytically; acquire, prepare and examine data; apply descriptive, predictive, causal and prescriptive methods; communicate evidence; evaluate models; and implement responsible, value-oriented analytics solutions in organizational settings.

Syllabus

1. Foundations of Business Analytics

Learning Objective: Define business analytics and its terminology; distinguish business intelligence, data science and descriptive, diagnostic, predictive and prescriptive analytics; explain evidence-based management, decision types and analytics-driven value creation.

Category: foundation

2. Business Problem Framing and Decision Models

Learning Objective: Translate managerial problems into analytical questions, hypotheses, objectives, decision variables, constraints, KPIs and measurable outcomes; use structured decision-making and stakeholder analysis to define scope and value.

Category: foundation

3. Data Foundations and Business Data Ecosystems

Learning Objective: Understand data types, measurement scales, databases, data warehouses, lakes, APIs and internal and external sources; assess data quality, lineage, metadata, master data and fitness for purpose; use foundational spreadsheet, SQL and data-access concepts.

Category: foundation

4. Analytics Processes, Projects and Technology Stack

Learning Objective: Apply CRISP-DM and related analytics lifecycles from business understanding through deployment; plan projects using agile experimentation, documentation and reproducibility; recognize the roles of BI platforms, programming tools, cloud services and analytics teams.

Category: core

5. Statistical Reasoning for Business

Learning Objective: Use probability, sampling, distributions, estimation, confidence intervals and hypothesis testing; identify bias, variability and uncertainty; distinguish statistical significance from practical and economic significance.

Category: core

6. Data Preparation and Exploratory Analytics

Learning Objective: Clean, integrate, transform and summarize data; address missing values, duplicates, outliers and inconsistent records; conduct exploratory data analysis using distributions, cross-tabulations, correlations, segmentation and diagnostic techniques.

Category: core

7. Data Visualization, Dashboards and Storytelling

Learning Objective: Select effective charts and dashboard designs; apply visual perception and information-design principles; avoid misleading displays; communicate analytical findings, uncertainty, recommendations and limitations to technical and managerial audiences.

Category: application

8. Predictive Analytics and Model Evaluation

Learning Objective: Develop and interpret regression, classification, time-series forecasting and basic segmentation models; perform feature selection, training and validation; evaluate models using appropriate error, classification, calibration and business-value metrics while controlling overfitting.

Category: core

9. Experimentation and Causal Analytics

Learning Objective: Distinguish correlation, prediction and causation; design A/B tests and randomized experiments; interpret treatment effects; recognize confounding, selection bias and threats to validity; understand introductory quasi-experimental approaches.

Category: advanced

10. Prescriptive Analytics and Decision Optimization

Learning Objective: Formulate business decisions using objectives, constraints and trade-offs; apply decision trees, linear optimization, sensitivity analysis, scenario analysis, simulation and risk analysis to recommend feasible actions under uncertainty.

Category: core

11. Machine Learning, AI and Generative AI

Learning Objective: Explain supervised and unsupervised learning, recommendation, natural language processing and generative AI; identify suitable business use cases; evaluate accuracy, explainability, hallucination, drift, human oversight and responsible human–AI collaboration.

Category: contemporary

12. Functional and Managerial Applications

Learning Objective: Apply analytics to marketing, customers, finance, operations, supply chains, human resources and risk; select relevant KPIs and methods; compare alternatives through cases and convert analytical evidence into implementable managerial recommendations.

Category: application

13. Analytics Implementation and Digital Transformation

Learning Objective: Operationalize models and dashboards within business processes; understand data pipelines, APIs, MLOps concepts, monitoring and model lifecycle management; assess build-versus-buy, adoption, change management, analytics maturity, ROI and alignment with digital strategy.

Category: strategic

14. Ethics, Privacy and Analytics Governance

Learning Objective: Evaluate fairness, bias, transparency, accountability, privacy, security and societal impacts; apply data and AI governance, access controls, auditability and regulatory principles; manage model, vendor and algorithmic risks responsibly.

Category: strategic

15. Industry Practice and Future Directions

Learning Objective: Examine real-time and augmented analytics, decision intelligence, automation, synthetic data, digital twins, edge analytics and evolving AI regulation; develop data literacy, critical thinking, portfolio, teamwork and communication capabilities expected in analytics careers.

Category: contemporary